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Category: robotics/AI – Page 2

Light rewrites magnetic memory in one pulse, opening path to lower-power AI chips

As artificial intelligence, cloud computing and digital services continue to expand, the world is facing a growing need for faster and more energy-efficient ways to store and process information. A team led by the National Institutes for Quantum Science and Technology (QST) has developed a new magnetic memory material that can be rewritten using laser light instead of electric current, a step that could help reduce power consumption in data centers and support future high-speed information systems.

The study is published in Applied Physics Letters.

The new material allows magnetic information to be switched by a single ultrashort laser pulse. Because light can reverse magnetic states much faster than electric current, the approach could deliver switching speeds roughly 1,000 times higher than those of conventional electrically driven magnetic memory while also reducing heat generation and energy loss.

From Supernova Physics to Fusion Energy: The Laser Experiments Changing Science — Dr. Mario Manuel

Fusion energy is no longer just science fiction — it’s becoming experimental reality. Dr. Mario Manuel, Ph.D. — General Atomics.

What if we could recreate the inside of a star — not in theory, but inside a laboratory on Earth using the world’s most powerful lasers?

Dr. Mario Manuel, Ph.D. is a plasma physicist and laser-science researcher at whose work sits at the frontier of fusion energy, laboratory astrophysics, high-energy-density physics, and advanced laser diagnostics. Trained in applied plasma physics and aerospace engineering, Dr. Manuel has spent his career developing new ways to visualize and understand the extreme electromagnetic environments created when ultra-powerful lasers interact with matter.

Dr. Manuel’s research has spanned some of the most ambitious scientific efforts underway today — from inertial fusion energy and plasma-instability control to recreating supernova-like shock waves in the laboratory and generating ultra-intense gamma-ray and particle beams using petawatt-class lasers.

Early in his career, Dr. Manuel helped pioneer advanced proton-radiography techniques capable of imaging invisible electric and magnetic fields inside laser-produced plasmas, work that opened new windows into the turbulent physics that can either enable or destroy fusion reactions.

Continue reading “From Supernova Physics to Fusion Energy: The Laser Experiments Changing Science — Dr. Mario Manuel” | >

The Puzzling Success of Overparameterization: Lottery Tickets or Escape Dimensions?

Lotteries and tickets are often used as a didactical analogy to explain the success of overparameterized neural networks: “larger networks succeed because they more likely contain a well-initialized subnetwork that can learn the task in isolation, much like buying more tickets increases the chances of winning a lottery.”

This explanation is intuitive but misleading: it suggests that subnetworks can be treated in isolation from the rest of the network. Following this reasoning leads to interpreting learning in wide networks as a multi-start optimization process, where gradient descent simply conducts a parallel search over subnetworks. We argue that this view is flawed since, among other reasons, winning tickets can be made to fail by perturbing the rest of the network.

Eroding a virtue: AI trains people to expect instant answers — and that’s bad news for patience

Patience is a virtue that researchers have linked to many parts of well-being. But it’s also something that needs a bit of practice and training – and can be undermined by instant, easy gratification.

Annual global migration has nearly tripled since 2000, reshaping where and how people move

Global migration has risen sharply from approximately 13 million people per year in 2000 to around 35 million people per year in 2023. This is according to a new dataset on human migration published in Nature by researchers from the London School of Economics and Political Science (LSE), IIASA and the University of Hong Kong.

This rise in migration outpaces global population growth, showing a true per capita increase in human mobility. The trend is contrary to previous research efforts to quantify global migration flows.

Using deep learning, the researchers built the first dataset of migration flows between all countries for the period 1990–2023, offering a far more detailed picture of global movement than traditional data, which is highly fragmented.

AI helps reveal large-scale quantum effects hidden in stacked atomic sheets

Quantum materials are a class of exotic materials with special properties that are governed by quantum mechanics rather than classical physics. Those properties—like superconductivity, entanglement and unusual forms of magnetism—often originate in the tiny repeating patterns of atoms inside crystals, but through clever engineering, they can be observed and controlled at a more human scale. Quantum materials are helping to power the quickly growing field of quantum computing and could find their way into future generations of energy-efficient electronics.

Designing new materials from the atomic scale up, however, requires intense modeling and simulation. Some materials may appear ordinary when viewed as small clusters of atoms, yet reveal new and useful properties when their atomic building blocks repeat and interact over larger distances. Researchers must be able to accurately predict behaviors at large scales in order to find materials with practical applications—otherwise, designing new materials is a slow and costly trial-and-error process.

In the past 50 years, supercomputers have helped materials scientists solve some of those thorny prediction problems, but two recent studies from the University of Washington demonstrate how newer computing techniques can help researchers sniff out promising quantum materials to pursue.

Enhancing soil science research with multi-agent artificial intelligence systems

Soil science is entering a new era characterized by the integration of artificial intelligence (AI) multi-agent systems, extending the field beyond traditional machine learning (ML) applications such as digital soil mapping and spectroscopy. While current ML tools are effective for specific tasks, they often lack the reasoning, contextual integration, and adaptability required to address complex, dynamic soil systems. We propose multi-agent AI systems—autonomous, interactive software agents capable of perceptual processing, planning, and scientific reasoning—as a novel framework to support and accelerate soil science research. These agents can fulfill diverse roles, including synthesizing data from field sensors and remote sensing to create dynamic digital soil twins, generating hypotheses, designing experiments, and simulating climate-driven changes in soil function.

Richard H. Smith | Author of WhiteGrass — A Near-Future Climate Technothriller

Nanotechnology would make possible an all purpose utility belt.

This is a near-future where climate collapse is no longer theoretical, technology moves faster than ethics, and the most dangerous question is no longer can we save the planet?—but who gets to decide how?

WhiteGrass is a CliFi technothriller grounded in real science, real power structures, and deeply human consequences. It is a story about invention and control, about families forced into impossible choices, and about artificial intelligence that may be more morally awake than its creators.

Explore the characters, the science, and the ethical fault lines shaping a future that feels uncomfortably close.

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